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  3. Manipulating Time Series Data in Python
  • 1

    Working with Time Series in Pandas

    Free

    This chapter lays the foundations to leverage the powerful time series functionality made available by how Pandas represents dates, in particular by the DateTimeIndex. You will learn how to create and manipulate date information and time series, and how to do calculations with time-aware DataFrames to shift your data in time or create period-specific returns.

  • 2

    Basic Time Series Metrics & Resampling

    This chapter dives deeper into the essential time series functionality made available through the pandas DataTimeIndex. It introduces resampling and how to compare different time series by normalizing their start points.

  • 3

    Window Functions: Rolling & Expanding Metrics

    This chapter will show you how to use window function to calculate time series metrics for both rolling and expanding windows.

  • 4

    Putting it all together: Building a value-weighted index

    This chapter combines the previous concepts by teaching you how to create a value-weighted index. This index uses market-cap data contained in the stock exchange listings to calculate weights and 2016 stock price information. Index performance is then compared against benchmarks to evaluate the performance of the index you created.

Initializing

Exercise

Cumulative return on $1,000 invested in google vs apple I

To put your new ability to do cumulative return calculations to practical use, let's compare how much $1,000 would be worth if invested in Google ('GOOG') or Apple ('AAPL') in 2010.

Instructions

100 XP

We have already imported pandas as pd, and matplotlib.pyplot as plt. We have also loaded Google and Apple stock prices in a variable data.

  • Define a variable investment with the value 1000.
  • Calculate returns by applying .pct_change() to data.
  • Add 1 to returns and assign this to returns_plus_one, then apply .cumprod() to returns_plus_one and assign the result to cumulative_return.
  • Multiply cumulative_return by investment, and plot the result.